Overview

Dataset statistics

Number of variables36
Number of observations10000
Missing cells60000
Missing cells (%)16.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.7 MiB
Average record size in memory288.0 B

Variable types

Numeric15
Categorical15
Unsupported6

Alerts

J has constant value "0"Constant
R has constant value "0"Constant
N has constant value "0"Constant
UPN has a high cardinality: 5795 distinct valuesHigh cardinality
EntryDate has a high cardinality: 261 distinct valuesHigh cardinality
TermlySessionsPossible is highly overall correlated with / and 1 other fieldsHigh correlation
/ is highly overall correlated with TermlySessionsPossible and 1 other fieldsHigh correlation
\ is highly overall correlated with TermlySessionsPossible and 1 other fieldsHigh correlation
EnrolStatus is highly imbalanced (56.1%)Imbalance
Surname has 10000 (100.0%) missing valuesMissing
Forename has 10000 (100.0%) missing valuesMissing
Middlenames has 10000 (100.0%) missing valuesMissing
PreferredSurname has 10000 (100.0%) missing valuesMissing
FormerSurname has 10000 (100.0%) missing valuesMissing
DoB has 10000 (100.0%) missing valuesMissing
UPN is uniformly distributedUniform
Surname is an unsupported type, check if it needs cleaning or further analysisUnsupported
Forename is an unsupported type, check if it needs cleaning or further analysisUnsupported
Middlenames is an unsupported type, check if it needs cleaning or further analysisUnsupported
PreferredSurname is an unsupported type, check if it needs cleaning or further analysisUnsupported
FormerSurname is an unsupported type, check if it needs cleaning or further analysisUnsupported
DoB is an unsupported type, check if it needs cleaning or further analysisUnsupported
B has 1174 (11.7%) zerosZeros
L has 792 (7.9%) zerosZeros
C has 966 (9.7%) zerosZeros
E has 3100 (31.0%) zerosZeros
I has 449 (4.5%) zerosZeros
M has 2052 (20.5%) zerosZeros
G has 2642 (26.4%) zerosZeros
O has 603 (6.0%) zerosZeros
D has 800 (8.0%) zerosZeros
X has 253 (2.5%) zerosZeros
Y has 1491 (14.9%) zerosZeros

Reproduction

Analysis started2023-06-26 14:00:50.212069
Analysis finished2023-06-26 14:01:22.155289
Duration31.94 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

Estab
Real number (ℝ)

Distinct9593
Distinct (%)95.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean190717.43
Minimum70267
Maximum294000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-06-26T15:01:22.231055image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum70267
5-th percentile131668.15
Q1166711.25
median191393.5
Q3214714.5
95-th percentile248592.1
Maximum294000
Range223733
Interquartile range (IQR)48003.25

Descriptive statistics

Standard deviation35413.867
Coefficient of variation (CV)0.18568763
Kurtosis-0.098381377
Mean190717.43
Median Absolute Deviation (MAD)24030
Skewness-0.055153459
Sum1.9071743 × 109
Variance1.254142 × 109
MonotonicityNot monotonic
2023-06-26T15:01:22.360939image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
165279 3
 
< 0.1%
206510 3
 
< 0.1%
196610 3
 
< 0.1%
139478 3
 
< 0.1%
144130 3
 
< 0.1%
200203 3
 
< 0.1%
178063 3
 
< 0.1%
177130 3
 
< 0.1%
193112 3
 
< 0.1%
208566 3
 
< 0.1%
Other values (9583) 9970
99.7%
ValueCountFrequency (%)
70267 1
< 0.1%
73231 1
< 0.1%
74264 1
< 0.1%
74777 1
< 0.1%
75297 1
< 0.1%
78602 1
< 0.1%
80759 1
< 0.1%
81168 1
< 0.1%
81332 1
< 0.1%
81789 1
< 0.1%
ValueCountFrequency (%)
294000 1
< 0.1%
293838 1
< 0.1%
293010 1
< 0.1%
292992 1
< 0.1%
292895 1
< 0.1%
292154 1
< 0.1%
292096 1
< 0.1%
292004 1
< 0.1%
291979 1
< 0.1%
291216 1
< 0.1%

UPN
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct5795
Distinct (%)58.0%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
1c5aba8e-9c02-4069-95a2-b3fac845e772
 
10
fa6e78fe-fbaf-4e92-9371-7782e47265b6
 
8
8f8f3359-3e05-4657-82dc-5c3bdaf1210a
 
8
18f7366a-35a1-402f-8163-a5bf98bf9b4b
 
8
eb4bc9f3-5c65-4b47-8ce3-c3e030b709f5
 
7
Other values (5790)
9959 

Length

Max length36
Median length36
Mean length36
Min length36

Characters and Unicode

Total characters360000
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3106 ?
Unique (%)31.1%

Sample

1st row2f339019-1f3f-4713-bb75-a8ca1bc7e238
2nd row3c24552c-9677-434e-aabc-917a7e1d4565
3rd row074c75ab-7f8c-4272-a5d7-fd1f4fde88ff
4th row7751bb61-7104-4271-820f-679ea0c8eeed
5th row26ffe55c-8c00-46a0-bb1a-84143011f672

Common Values

ValueCountFrequency (%)
1c5aba8e-9c02-4069-95a2-b3fac845e772 10
 
0.1%
fa6e78fe-fbaf-4e92-9371-7782e47265b6 8
 
0.1%
8f8f3359-3e05-4657-82dc-5c3bdaf1210a 8
 
0.1%
18f7366a-35a1-402f-8163-a5bf98bf9b4b 8
 
0.1%
eb4bc9f3-5c65-4b47-8ce3-c3e030b709f5 7
 
0.1%
ab482fc5-e328-4d44-8b2d-ce1248d05c8d 7
 
0.1%
d13df8c0-255f-448f-b31b-dd91701073c1 7
 
0.1%
cda2c1f5-4fca-47d9-996b-fec5b9fd9dbb 7
 
0.1%
24ebf25f-1865-4006-876d-33341b735172 7
 
0.1%
21d1176c-5f44-473b-af2e-7a45af8f92ca 7
 
0.1%
Other values (5785) 9924
99.2%

Length

2023-06-26T15:01:22.478011image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1c5aba8e-9c02-4069-95a2-b3fac845e772 10
 
0.1%
18f7366a-35a1-402f-8163-a5bf98bf9b4b 8
 
0.1%
fa6e78fe-fbaf-4e92-9371-7782e47265b6 8
 
0.1%
8f8f3359-3e05-4657-82dc-5c3bdaf1210a 8
 
0.1%
ab482fc5-e328-4d44-8b2d-ce1248d05c8d 7
 
0.1%
d13df8c0-255f-448f-b31b-dd91701073c1 7
 
0.1%
cda2c1f5-4fca-47d9-996b-fec5b9fd9dbb 7
 
0.1%
24ebf25f-1865-4006-876d-33341b735172 7
 
0.1%
21d1176c-5f44-473b-af2e-7a45af8f92ca 7
 
0.1%
eb4bc9f3-5c65-4b47-8ce3-c3e030b709f5 7
 
0.1%
Other values (5785) 9924
99.2%

Most occurring characters

ValueCountFrequency (%)
- 40000
 
11.1%
4 28697
 
8.0%
8 21514
 
6.0%
9 21274
 
5.9%
b 21261
 
5.9%
a 21233
 
5.9%
6 19164
 
5.3%
5 18996
 
5.3%
0 18878
 
5.2%
1 18854
 
5.2%
Other values (7) 130129
36.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 203556
56.5%
Lowercase Letter 116444
32.3%
Dash Punctuation 40000
 
11.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 28697
14.1%
8 21514
10.6%
9 21274
10.5%
6 19164
9.4%
5 18996
9.3%
0 18878
9.3%
1 18854
9.3%
3 18758
9.2%
7 18737
9.2%
2 18684
9.2%
Lowercase Letter
ValueCountFrequency (%)
b 21261
18.3%
a 21233
18.2%
e 18592
16.0%
d 18586
16.0%
c 18395
15.8%
f 18377
15.8%
Dash Punctuation
ValueCountFrequency (%)
- 40000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 243556
67.7%
Latin 116444
32.3%

Most frequent character per script

Common
ValueCountFrequency (%)
- 40000
16.4%
4 28697
11.8%
8 21514
8.8%
9 21274
8.7%
6 19164
7.9%
5 18996
7.8%
0 18878
7.8%
1 18854
7.7%
3 18758
7.7%
7 18737
7.7%
Latin
ValueCountFrequency (%)
b 21261
18.3%
a 21233
18.2%
e 18592
16.0%
d 18586
16.0%
c 18395
15.8%
f 18377
15.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 360000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 40000
 
11.1%
4 28697
 
8.0%
8 21514
 
6.0%
9 21274
 
5.9%
b 21261
 
5.9%
a 21233
 
5.9%
6 19164
 
5.3%
5 18996
 
5.3%
0 18878
 
5.2%
1 18854
 
5.2%
Other values (7) 130129
36.1%

Surname
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size78.2 KiB

Forename
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size78.2 KiB

Middlenames
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size78.2 KiB

PreferredSurname
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size78.2 KiB

FormerSurname
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size78.2 KiB

Gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
F
5154 
M
4846 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowF
2nd rowF
3rd rowM
4th rowM
5th rowM

Common Values

ValueCountFrequency (%)
F 5154
51.5%
M 4846
48.5%

Length

2023-06-26T15:01:22.583113image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-26T15:01:22.690252image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
f 5154
51.5%
m 4846
48.5%

Most occurring characters

ValueCountFrequency (%)
F 5154
51.5%
M 4846
48.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 10000
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
F 5154
51.5%
M 4846
48.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 10000
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
F 5154
51.5%
M 4846
48.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
F 5154
51.5%
M 4846
48.5%

DoB
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size78.2 KiB

EnrolStatus
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
C
8290 
Leaver
1654 
S
 
56

Length

Max length6
Median length1
Mean length1.827
Min length1

Characters and Unicode

Total characters18270
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowC
2nd rowC
3rd rowLeaver
4th rowC
5th rowLeaver

Common Values

ValueCountFrequency (%)
C 8290
82.9%
Leaver 1654
 
16.5%
S 56
 
0.6%

Length

2023-06-26T15:01:22.795143image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-26T15:01:22.916682image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
c 8290
82.9%
leaver 1654
 
16.5%
s 56
 
0.6%

Most occurring characters

ValueCountFrequency (%)
C 8290
45.4%
e 3308
 
18.1%
L 1654
 
9.1%
a 1654
 
9.1%
v 1654
 
9.1%
r 1654
 
9.1%
S 56
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 10000
54.7%
Lowercase Letter 8270
45.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 3308
40.0%
a 1654
20.0%
v 1654
20.0%
r 1654
20.0%
Uppercase Letter
ValueCountFrequency (%)
C 8290
82.9%
L 1654
 
16.5%
S 56
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 18270
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 8290
45.4%
e 3308
 
18.1%
L 1654
 
9.1%
a 1654
 
9.1%
v 1654
 
9.1%
r 1654
 
9.1%
S 56
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 18270
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C 8290
45.4%
e 3308
 
18.1%
L 1654
 
9.1%
a 1654
 
9.1%
v 1654
 
9.1%
r 1654
 
9.1%
S 56
 
0.3%

EntryDate
Categorical

Distinct261
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
2020-09-03 00:00:00
1111 
2019-09-04 00:00:00
1088 
2017-09-06 00:00:00
1019 
2016-09-05 00:00:00
920 
2018-09-07 00:00:00
705 
Other values (256)
5157 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters190000
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique83 ?
Unique (%)0.8%

Sample

1st row2016-09-01 00:00:00
2nd row2016-09-05 00:00:00
3rd row2016-09-02 00:00:00
4th row2018-09-04 00:00:00
5th row2016-09-01 00:00:00

Common Values

ValueCountFrequency (%)
2020-09-03 00:00:00 1111
 
11.1%
2019-09-04 00:00:00 1088
 
10.9%
2017-09-06 00:00:00 1019
 
10.2%
2016-09-05 00:00:00 920
 
9.2%
2018-09-07 00:00:00 705
 
7.0%
2018-09-06 00:00:00 589
 
5.9%
2016-09-01 00:00:00 494
 
4.9%
2020-09-02 00:00:00 470
 
4.7%
2018-09-05 00:00:00 420
 
4.2%
2020-09-01 00:00:00 376
 
3.8%
Other values (251) 2808
28.1%

Length

2023-06-26T15:01:23.025666image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
00:00:00 10000
50.0%
2020-09-03 1111
 
5.6%
2019-09-04 1088
 
5.4%
2017-09-06 1019
 
5.1%
2016-09-05 920
 
4.6%
2018-09-07 705
 
3.5%
2018-09-06 589
 
2.9%
2016-09-01 494
 
2.5%
2020-09-02 470
 
2.4%
2018-09-05 420
 
2.1%
Other values (252) 3184
 
15.9%

Most occurring characters

ValueCountFrequency (%)
0 91733
48.3%
- 20000
 
10.5%
: 20000
 
10.5%
2 13484
 
7.1%
9 11489
 
6.0%
10000
 
5.3%
1 9693
 
5.1%
6 3228
 
1.7%
8 2557
 
1.3%
7 2497
 
1.3%
Other values (3) 5319
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 140000
73.7%
Dash Punctuation 20000
 
10.5%
Other Punctuation 20000
 
10.5%
Space Separator 10000
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 91733
65.5%
2 13484
 
9.6%
9 11489
 
8.2%
1 9693
 
6.9%
6 3228
 
2.3%
8 2557
 
1.8%
7 2497
 
1.8%
4 1886
 
1.3%
5 1732
 
1.2%
3 1701
 
1.2%
Dash Punctuation
ValueCountFrequency (%)
- 20000
100.0%
Other Punctuation
ValueCountFrequency (%)
: 20000
100.0%
Space Separator
ValueCountFrequency (%)
10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 190000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 91733
48.3%
- 20000
 
10.5%
: 20000
 
10.5%
2 13484
 
7.1%
9 11489
 
6.0%
10000
 
5.3%
1 9693
 
5.1%
6 3228
 
1.7%
8 2557
 
1.3%
7 2497
 
1.3%
Other values (3) 5319
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 190000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 91733
48.3%
- 20000
 
10.5%
: 20000
 
10.5%
2 13484
 
7.1%
9 11489
 
6.0%
10000
 
5.3%
1 9693
 
5.1%
6 3228
 
1.7%
8 2557
 
1.3%
7 2497
 
1.3%
Other values (3) 5319
 
2.8%

NCyearActual
Categorical

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
8
1983 
9
1951 
11
1904 
7
1847 
10
1389 
Other values (4)
926 

Length

Max length6
Median length1
Mean length1.6571
Min length1

Characters and Unicode

Total characters16571
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row14
3rd rowLeaver
4th row8
5th row10

Common Values

ValueCountFrequency (%)
8 1983
19.8%
9 1951
19.5%
11 1904
19.0%
7 1847
18.5%
10 1389
13.9%
Leaver 588
 
5.9%
12 191
 
1.9%
13 105
 
1.1%
14 42
 
0.4%

Length

2023-06-26T15:01:23.136763image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-26T15:01:23.275044image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
8 1983
19.8%
9 1951
19.5%
11 1904
19.0%
7 1847
18.5%
10 1389
13.9%
leaver 588
 
5.9%
12 191
 
1.9%
13 105
 
1.1%
14 42
 
0.4%

Most occurring characters

ValueCountFrequency (%)
1 5535
33.4%
8 1983
 
12.0%
9 1951
 
11.8%
7 1847
 
11.1%
0 1389
 
8.4%
e 1176
 
7.1%
L 588
 
3.5%
a 588
 
3.5%
v 588
 
3.5%
r 588
 
3.5%
Other values (3) 338
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13043
78.7%
Lowercase Letter 2940
 
17.7%
Uppercase Letter 588
 
3.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 5535
42.4%
8 1983
 
15.2%
9 1951
 
15.0%
7 1847
 
14.2%
0 1389
 
10.6%
2 191
 
1.5%
3 105
 
0.8%
4 42
 
0.3%
Lowercase Letter
ValueCountFrequency (%)
e 1176
40.0%
a 588
20.0%
v 588
20.0%
r 588
20.0%
Uppercase Letter
ValueCountFrequency (%)
L 588
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 13043
78.7%
Latin 3528
 
21.3%

Most frequent character per script

Common
ValueCountFrequency (%)
1 5535
42.4%
8 1983
 
15.2%
9 1951
 
15.0%
7 1847
 
14.2%
0 1389
 
10.6%
2 191
 
1.5%
3 105
 
0.8%
4 42
 
0.3%
Latin
ValueCountFrequency (%)
e 1176
33.3%
L 588
16.7%
a 588
16.7%
v 588
16.7%
r 588
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16571
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 5535
33.4%
8 1983
 
12.0%
9 1951
 
11.8%
7 1847
 
11.1%
0 1389
 
8.4%
e 1176
 
7.1%
L 588
 
3.5%
a 588
 
3.5%
v 588
 
3.5%
r 588
 
3.5%
Other values (3) 338
 
2.0%

TermlySessionsPossible
Real number (ℝ)

Distinct87
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean123.6356
Minimum44
Maximum144
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-06-26T15:01:23.406889image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum44
5-th percentile94
Q1114
median127
Q3136
95-th percentile142
Maximum144
Range100
Interquartile range (IQR)22

Descriptive statistics

Standard deviation15.24992
Coefficient of variation (CV)0.12334571
Kurtosis0.70196689
Mean123.6356
Median Absolute Deviation (MAD)10
Skewness-0.94837163
Sum1236356
Variance232.56007
MonotonicityNot monotonic
2023-06-26T15:01:23.537147image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
141 325
 
3.2%
140 322
 
3.2%
142 322
 
3.2%
139 316
 
3.2%
143 316
 
3.2%
138 313
 
3.1%
134 304
 
3.0%
129 291
 
2.9%
137 281
 
2.8%
133 275
 
2.8%
Other values (77) 6935
69.3%
ValueCountFrequency (%)
44 1
 
< 0.1%
55 1
 
< 0.1%
57 1
 
< 0.1%
58 2
 
< 0.1%
60 4
< 0.1%
62 5
0.1%
63 2
 
< 0.1%
64 2
 
< 0.1%
66 3
< 0.1%
67 4
< 0.1%
ValueCountFrequency (%)
144 140
1.4%
143 316
3.2%
142 322
3.2%
141 325
3.2%
140 322
3.2%
139 316
3.2%
138 313
3.1%
137 281
2.8%
136 275
2.8%
135 248
2.5%

/
Real number (ℝ)

Distinct70
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55.9976
Minimum0
Maximum72
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-06-26T15:01:23.662288image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile32
Q149
median59
Q366
95-th percentile71
Maximum72
Range72
Interquartile range (IQR)17

Descriptive statistics

Standard deviation12.182202
Coefficient of variation (CV)0.21754864
Kurtosis0.74894707
Mean55.9976
Median Absolute Deviation (MAD)8
Skewness-0.98817148
Sum559976
Variance148.40603
MonotonicityNot monotonic
2023-06-26T15:01:23.975885image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
67 423
 
4.2%
70 402
 
4.0%
69 394
 
3.9%
64 391
 
3.9%
68 390
 
3.9%
71 386
 
3.9%
66 375
 
3.8%
65 362
 
3.6%
61 352
 
3.5%
63 341
 
3.4%
Other values (60) 6184
61.8%
ValueCountFrequency (%)
0 1
 
< 0.1%
3 2
 
< 0.1%
4 1
 
< 0.1%
5 1
 
< 0.1%
7 3
< 0.1%
8 3
< 0.1%
9 5
0.1%
10 6
0.1%
11 3
< 0.1%
12 6
0.1%
ValueCountFrequency (%)
72 215
2.1%
71 386
3.9%
70 402
4.0%
69 394
3.9%
68 390
3.9%
67 423
4.2%
66 375
3.8%
65 362
3.6%
64 391
3.9%
63 341
3.4%

\
Real number (ℝ)

Distinct69
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56.8507
Minimum1
Maximum72
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-06-26T15:01:24.118191image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile35
Q150
median59
Q366
95-th percentile71
Maximum72
Range71
Interquartile range (IQR)16

Descriptive statistics

Standard deviation11.482613
Coefficient of variation (CV)0.20197839
Kurtosis0.73700378
Mean56.8507
Median Absolute Deviation (MAD)8
Skewness-0.97786056
Sum568507
Variance131.85039
MonotonicityNot monotonic
2023-06-26T15:01:24.245272image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
71 433
 
4.3%
66 432
 
4.3%
68 430
 
4.3%
69 418
 
4.2%
67 416
 
4.2%
70 406
 
4.1%
65 399
 
4.0%
64 377
 
3.8%
61 368
 
3.7%
63 359
 
3.6%
Other values (59) 5962
59.6%
ValueCountFrequency (%)
1 1
 
< 0.1%
2 1
 
< 0.1%
4 1
 
< 0.1%
6 1
 
< 0.1%
8 1
 
< 0.1%
9 1
 
< 0.1%
10 3
< 0.1%
11 3
< 0.1%
12 4
< 0.1%
13 6
0.1%
ValueCountFrequency (%)
72 194
1.9%
71 433
4.3%
70 406
4.1%
69 418
4.2%
68 430
4.3%
67 416
4.2%
66 432
4.3%
65 399
4.0%
64 377
3.8%
63 359
3.6%

B
Real number (ℝ)

Distinct14
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7069
Minimum0
Maximum13
Zeros1174
Zeros (%)11.7%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-06-26T15:01:24.359473image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q34
95-th percentile7
Maximum13
Range13
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.069546
Coefficient of variation (CV)0.76454469
Kurtosis0.60389316
Mean2.7069
Median Absolute Deviation (MAD)1
Skewness0.88996062
Sum27069
Variance4.2830207
MonotonicityNot monotonic
2023-06-26T15:01:24.458797image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
1 2216
22.2%
2 1928
19.3%
3 1681
16.8%
0 1174
11.7%
4 1109
11.1%
5 796
 
8.0%
6 580
 
5.8%
7 251
 
2.5%
8 146
 
1.5%
9 78
 
0.8%
Other values (4) 41
 
0.4%
ValueCountFrequency (%)
0 1174
11.7%
1 2216
22.2%
2 1928
19.3%
3 1681
16.8%
4 1109
11.1%
5 796
 
8.0%
6 580
 
5.8%
7 251
 
2.5%
8 146
 
1.5%
9 78
 
0.8%
ValueCountFrequency (%)
13 2
 
< 0.1%
12 2
 
< 0.1%
11 11
 
0.1%
10 26
 
0.3%
9 78
 
0.8%
8 146
 
1.5%
7 251
 
2.5%
6 580
5.8%
5 796
8.0%
4 1109
11.1%

J
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
0
10000 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 10000
100.0%

Length

2023-06-26T15:01:24.560158image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-26T15:01:24.655078image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 10000
100.0%

Most occurring characters

ValueCountFrequency (%)
0 10000
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 10000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 10000
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 10000
100.0%

L
Real number (ℝ)

Distinct19
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.9989
Minimum0
Maximum18
Zeros792
Zeros (%)7.9%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-06-26T15:01:24.735555image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q36
95-th percentile10
Maximum18
Range18
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.0426345
Coefficient of variation (CV)0.76086786
Kurtosis0.65342057
Mean3.9989
Median Absolute Deviation (MAD)2
Skewness0.92890151
Sum39989
Variance9.2576246
MonotonicityNot monotonic
2023-06-26T15:01:24.845505image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
1 1577
15.8%
2 1479
14.8%
3 1290
12.9%
4 1156
11.6%
5 914
9.1%
6 828
8.3%
0 792
7.9%
7 585
 
5.9%
8 463
 
4.6%
9 348
 
3.5%
Other values (9) 568
 
5.7%
ValueCountFrequency (%)
0 792
7.9%
1 1577
15.8%
2 1479
14.8%
3 1290
12.9%
4 1156
11.6%
5 914
9.1%
6 828
8.3%
7 585
 
5.9%
8 463
 
4.6%
9 348
 
3.5%
ValueCountFrequency (%)
18 4
 
< 0.1%
17 6
 
0.1%
16 5
 
0.1%
15 19
 
0.2%
14 33
 
0.3%
13 57
 
0.6%
12 81
 
0.8%
11 147
1.5%
10 216
2.2%
9 348
3.5%

P
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
0
5377 
1
4385 
2
 
238

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 5377
53.8%
1 4385
43.9%
2 238
 
2.4%

Length

2023-06-26T15:01:24.985208image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-26T15:01:25.105517image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 5377
53.8%
1 4385
43.9%
2 238
 
2.4%

Most occurring characters

ValueCountFrequency (%)
0 5377
53.8%
1 4385
43.9%
2 238
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5377
53.8%
1 4385
43.9%
2 238
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
Common 10000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5377
53.8%
1 4385
43.9%
2 238
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5377
53.8%
1 4385
43.9%
2 238
 
2.4%

V
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
1
4537 
0
3322 
2
1764 
3
 
361
4
 
16

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row1
3rd row2
4th row0
5th row2

Common Values

ValueCountFrequency (%)
1 4537
45.4%
0 3322
33.2%
2 1764
 
17.6%
3 361
 
3.6%
4 16
 
0.2%

Length

2023-06-26T15:01:25.208741image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-26T15:01:25.346829image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 4537
45.4%
0 3322
33.2%
2 1764
 
17.6%
3 361
 
3.6%
4 16
 
0.2%

Most occurring characters

ValueCountFrequency (%)
1 4537
45.4%
0 3322
33.2%
2 1764
 
17.6%
3 361
 
3.6%
4 16
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 4537
45.4%
0 3322
33.2%
2 1764
 
17.6%
3 361
 
3.6%
4 16
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 10000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 4537
45.4%
0 3322
33.2%
2 1764
 
17.6%
3 361
 
3.6%
4 16
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 4537
45.4%
0 3322
33.2%
2 1764
 
17.6%
3 361
 
3.6%
4 16
 
0.2%

W
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
1
4789 
0
4030 
2
1105 
3
 
76

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row2
5th row0

Common Values

ValueCountFrequency (%)
1 4789
47.9%
0 4030
40.3%
2 1105
 
11.1%
3 76
 
0.8%

Length

2023-06-26T15:01:25.446645image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-26T15:01:25.558260image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 4789
47.9%
0 4030
40.3%
2 1105
 
11.1%
3 76
 
0.8%

Most occurring characters

ValueCountFrequency (%)
1 4789
47.9%
0 4030
40.3%
2 1105
 
11.1%
3 76
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 4789
47.9%
0 4030
40.3%
2 1105
 
11.1%
3 76
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Common 10000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 4789
47.9%
0 4030
40.3%
2 1105
 
11.1%
3 76
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 4789
47.9%
0 4030
40.3%
2 1105
 
11.1%
3 76
 
0.8%

C
Real number (ℝ)

Distinct18
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4872
Minimum0
Maximum17
Zeros966
Zeros (%)9.7%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-06-26T15:01:25.654848image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile9
Maximum17
Range17
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.6701965
Coefficient of variation (CV)0.7657136
Kurtosis0.68478278
Mean3.4872
Median Absolute Deviation (MAD)2
Skewness0.9195914
Sum34872
Variance7.1299492
MonotonicityNot monotonic
2023-06-26T15:01:25.758279image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
1 1728
17.3%
2 1611
16.1%
3 1475
14.8%
4 1135
11.3%
0 966
9.7%
5 928
9.3%
6 767
7.7%
7 512
 
5.1%
8 375
 
3.8%
9 208
 
2.1%
Other values (8) 295
 
2.9%
ValueCountFrequency (%)
0 966
9.7%
1 1728
17.3%
2 1611
16.1%
3 1475
14.8%
4 1135
11.3%
5 928
9.3%
6 767
7.7%
7 512
 
5.1%
8 375
 
3.8%
9 208
 
2.1%
ValueCountFrequency (%)
17 2
 
< 0.1%
16 3
 
< 0.1%
15 4
 
< 0.1%
14 9
 
0.1%
13 20
 
0.2%
12 36
 
0.4%
11 89
 
0.9%
10 132
 
1.3%
9 208
2.1%
8 375
3.8%

E
Real number (ℝ)

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.9909
Minimum0
Maximum5
Zeros3100
Zeros (%)31.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-06-26T15:01:25.881322image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile2
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.85830697
Coefficient of variation (CV)0.8661893
Kurtosis0.26207789
Mean0.9909
Median Absolute Deviation (MAD)1
Skewness0.68185202
Sum9909
Variance0.73669086
MonotonicityNot monotonic
2023-06-26T15:01:26.031157image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 4452
44.5%
0 3100
31.0%
2 1952
19.5%
3 434
 
4.3%
4 59
 
0.6%
5 3
 
< 0.1%
ValueCountFrequency (%)
0 3100
31.0%
1 4452
44.5%
2 1952
19.5%
3 434
 
4.3%
4 59
 
0.6%
5 3
 
< 0.1%
ValueCountFrequency (%)
5 3
 
< 0.1%
4 59
 
0.6%
3 434
 
4.3%
2 1952
19.5%
1 4452
44.5%
0 3100
31.0%

H
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
0
7041 
1
2937 
2
 
22

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 7041
70.4%
1 2937
29.4%
2 22
 
0.2%

Length

2023-06-26T15:01:26.167171image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-26T15:01:26.290582image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 7041
70.4%
1 2937
29.4%
2 22
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 7041
70.4%
1 2937
29.4%
2 22
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 7041
70.4%
1 2937
29.4%
2 22
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 10000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 7041
70.4%
1 2937
29.4%
2 22
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 7041
70.4%
1 2937
29.4%
2 22
 
0.2%

I
Real number (ℝ)

Distinct32
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.0154
Minimum0
Maximum31
Zeros449
Zeros (%)4.5%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-06-26T15:01:26.389334image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median6
Q310
95-th percentile17
Maximum31
Range31
Interquartile range (IQR)7

Descriptive statistics

Standard deviation5.2936155
Coefficient of variation (CV)0.75457073
Kurtosis0.71028321
Mean7.0154
Median Absolute Deviation (MAD)4
Skewness0.95308116
Sum70154
Variance28.022365
MonotonicityNot monotonic
2023-06-26T15:01:26.511156image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
1 935
 
9.3%
2 864
 
8.6%
3 839
 
8.4%
4 808
 
8.1%
5 741
 
7.4%
6 731
 
7.3%
7 681
 
6.8%
8 600
 
6.0%
9 538
 
5.4%
10 504
 
5.0%
Other values (22) 2759
27.6%
ValueCountFrequency (%)
0 449
4.5%
1 935
9.3%
2 864
8.6%
3 839
8.4%
4 808
8.1%
5 741
7.4%
6 731
7.3%
7 681
6.8%
8 600
6.0%
9 538
5.4%
ValueCountFrequency (%)
31 2
 
< 0.1%
30 6
 
0.1%
29 4
 
< 0.1%
28 7
 
0.1%
27 6
 
0.1%
26 11
 
0.1%
25 14
 
0.1%
24 17
0.2%
23 26
0.3%
22 37
0.4%

M
Real number (ℝ)

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.478
Minimum0
Maximum7
Zeros2052
Zeros (%)20.5%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-06-26T15:01:26.625193image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile4
Maximum7
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1818865
Coefficient of variation (CV)0.79965257
Kurtosis0.61625538
Mean1.478
Median Absolute Deviation (MAD)1
Skewness0.8319615
Sum14780
Variance1.3968557
MonotonicityNot monotonic
2023-06-26T15:01:26.722874image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1 3760
37.6%
2 2345
23.4%
0 2052
20.5%
3 1236
 
12.4%
4 454
 
4.5%
5 116
 
1.2%
6 33
 
0.3%
7 4
 
< 0.1%
ValueCountFrequency (%)
0 2052
20.5%
1 3760
37.6%
2 2345
23.4%
3 1236
 
12.4%
4 454
 
4.5%
5 116
 
1.2%
6 33
 
0.3%
7 4
 
< 0.1%
ValueCountFrequency (%)
7 4
 
< 0.1%
6 33
 
0.3%
5 116
 
1.2%
4 454
 
4.5%
3 1236
 
12.4%
2 2345
23.4%
1 3760
37.6%
0 2052
20.5%

R
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
0
10000 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 10000
100.0%

Length

2023-06-26T15:01:26.849483image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-26T15:01:26.964898image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 10000
100.0%

Most occurring characters

ValueCountFrequency (%)
0 10000
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 10000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 10000
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 10000
100.0%

S
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
0
7131 
1
2859 
2
 
10

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 7131
71.3%
1 2859
28.6%
2 10
 
0.1%

Length

2023-06-26T15:01:27.148783image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-26T15:01:27.350323image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 7131
71.3%
1 2859
28.6%
2 10
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 7131
71.3%
1 2859
28.6%
2 10
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 7131
71.3%
1 2859
28.6%
2 10
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 10000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 7131
71.3%
1 2859
28.6%
2 10
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 7131
71.3%
1 2859
28.6%
2 10
 
0.1%

T
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
0
5578 
1
4236 
2
 
186

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 5578
55.8%
1 4236
42.4%
2 186
 
1.9%

Length

2023-06-26T15:01:27.453957image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-26T15:01:27.562267image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 5578
55.8%
1 4236
42.4%
2 186
 
1.9%

Most occurring characters

ValueCountFrequency (%)
0 5578
55.8%
1 4236
42.4%
2 186
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5578
55.8%
1 4236
42.4%
2 186
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
Common 10000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5578
55.8%
1 4236
42.4%
2 186
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5578
55.8%
1 4236
42.4%
2 186
 
1.9%

G
Real number (ℝ)

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1658
Minimum0
Maximum6
Zeros2642
Zeros (%)26.4%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-06-26T15:01:27.661297image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile3
Maximum6
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation0.97355284
Coefficient of variation (CV)0.83509422
Kurtosis0.37489391
Mean1.1658
Median Absolute Deviation (MAD)1
Skewness0.7422001
Sum11658
Variance0.94780514
MonotonicityNot monotonic
2023-06-26T15:01:27.747163image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 4237
42.4%
0 2642
26.4%
2 2143
21.4%
3 800
 
8.0%
4 157
 
1.6%
5 19
 
0.2%
6 2
 
< 0.1%
ValueCountFrequency (%)
0 2642
26.4%
1 4237
42.4%
2 2143
21.4%
3 800
 
8.0%
4 157
 
1.6%
5 19
 
0.2%
6 2
 
< 0.1%
ValueCountFrequency (%)
6 2
 
< 0.1%
5 19
 
0.2%
4 157
 
1.6%
3 800
 
8.0%
2 2143
21.4%
1 4237
42.4%
0 2642
26.4%

N
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
0
10000 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 10000
100.0%

Length

2023-06-26T15:01:27.880326image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-26T15:01:27.991184image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 10000
100.0%

Most occurring characters

ValueCountFrequency (%)
0 10000
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 10000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 10000
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 10000
100.0%

O
Real number (ℝ)

Distinct25
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.5213
Minimum0
Maximum24
Zeros603
Zeros (%)6.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-06-26T15:01:28.088339image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q38
95-th percentile14
Maximum24
Range24
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.2098122
Coefficient of variation (CV)0.76246757
Kurtosis0.76660652
Mean5.5213
Median Absolute Deviation (MAD)3
Skewness0.97668287
Sum55213
Variance17.722519
MonotonicityNot monotonic
2023-06-26T15:01:28.232446image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
1 1123
11.2%
2 1084
10.8%
3 1072
10.7%
4 951
9.5%
5 949
9.5%
6 735
7.3%
7 701
7.0%
0 603
 
6.0%
8 578
 
5.8%
9 470
 
4.7%
Other values (15) 1734
17.3%
ValueCountFrequency (%)
0 603
6.0%
1 1123
11.2%
2 1084
10.8%
3 1072
10.7%
4 951
9.5%
5 949
9.5%
6 735
7.3%
7 701
7.0%
8 578
5.8%
9 470
4.7%
ValueCountFrequency (%)
24 4
 
< 0.1%
23 7
 
0.1%
22 7
 
0.1%
21 10
 
0.1%
20 20
 
0.2%
19 37
 
0.4%
18 44
 
0.4%
17 56
0.6%
16 78
0.8%
15 118
1.2%

U
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
0
6027 
1
3896 
2
 
77

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 6027
60.3%
1 3896
39.0%
2 77
 
0.8%

Length

2023-06-26T15:01:28.378272image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-26T15:01:28.564285image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 6027
60.3%
1 3896
39.0%
2 77
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 6027
60.3%
1 3896
39.0%
2 77
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 6027
60.3%
1 3896
39.0%
2 77
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Common 10000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 6027
60.3%
1 3896
39.0%
2 77
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 6027
60.3%
1 3896
39.0%
2 77
 
0.8%

D
Real number (ℝ)

Distinct21
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.933
Minimum0
Maximum20
Zeros800
Zeros (%)8.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-06-26T15:01:28.716902image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q36
95-th percentile10
Maximum20
Range20
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.9989682
Coefficient of variation (CV)0.76251417
Kurtosis0.95463821
Mean3.933
Median Absolute Deviation (MAD)2
Skewness0.98242904
Sum39330
Variance8.9938104
MonotonicityNot monotonic
2023-06-26T15:01:28.958608image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
1 1599
16.0%
2 1470
14.7%
3 1334
13.3%
4 1180
11.8%
5 955
9.6%
0 800
8.0%
6 746
7.5%
7 652
6.5%
8 427
 
4.3%
9 315
 
3.1%
Other values (11) 522
 
5.2%
ValueCountFrequency (%)
0 800
8.0%
1 1599
16.0%
2 1470
14.7%
3 1334
13.3%
4 1180
11.8%
5 955
9.6%
6 746
7.5%
7 652
6.5%
8 427
 
4.3%
9 315
 
3.1%
ValueCountFrequency (%)
20 2
 
< 0.1%
19 1
 
< 0.1%
18 2
 
< 0.1%
17 8
 
0.1%
16 6
 
0.1%
15 16
 
0.2%
14 34
 
0.3%
13 52
0.5%
12 82
0.8%
11 110
1.1%

X
Real number (ℝ)

Distinct57
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.5496
Minimum0
Maximum63
Zeros253
Zeros (%)2.5%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-06-26T15:01:29.205312image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q15
median11
Q318
95-th percentile30
Maximum63
Range63
Interquartile range (IQR)13

Descriptive statistics

Standard deviation9.3463402
Coefficient of variation (CV)0.74475204
Kurtosis0.90610255
Mean12.5496
Median Absolute Deviation (MAD)6
Skewness0.98089479
Sum125496
Variance87.354075
MonotonicityNot monotonic
2023-06-26T15:01:29.354510image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 509
 
5.1%
1 502
 
5.0%
6 485
 
4.9%
7 482
 
4.8%
2 465
 
4.7%
4 459
 
4.6%
5 455
 
4.5%
8 447
 
4.5%
10 431
 
4.3%
9 427
 
4.3%
Other values (47) 5338
53.4%
ValueCountFrequency (%)
0 253
2.5%
1 502
5.0%
2 465
4.7%
3 509
5.1%
4 459
4.6%
5 455
4.5%
6 485
4.9%
7 482
4.8%
8 447
4.5%
9 427
4.3%
ValueCountFrequency (%)
63 1
 
< 0.1%
57 2
 
< 0.1%
54 1
 
< 0.1%
53 3
< 0.1%
52 2
 
< 0.1%
51 3
< 0.1%
50 4
< 0.1%
49 5
0.1%
48 6
0.1%
47 7
0.1%

Y
Real number (ℝ)

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1561
Minimum0
Maximum10
Zeros1491
Zeros (%)14.9%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-06-26T15:01:29.570421image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile5
Maximum10
Range10
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.688555
Coefficient of variation (CV)0.78315244
Kurtosis0.78097078
Mean2.1561
Median Absolute Deviation (MAD)1
Skewness0.9190962
Sum21561
Variance2.8512179
MonotonicityNot monotonic
2023-06-26T15:01:29.691948image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
1 2694
26.9%
2 2232
22.3%
3 1615
16.2%
0 1491
14.9%
4 981
 
9.8%
5 558
 
5.6%
6 249
 
2.5%
7 118
 
1.2%
8 36
 
0.4%
9 24
 
0.2%
ValueCountFrequency (%)
0 1491
14.9%
1 2694
26.9%
2 2232
22.3%
3 1615
16.2%
4 981
 
9.8%
5 558
 
5.6%
6 249
 
2.5%
7 118
 
1.2%
8 36
 
0.4%
9 24
 
0.2%
ValueCountFrequency (%)
10 2
 
< 0.1%
9 24
 
0.2%
8 36
 
0.4%
7 118
 
1.2%
6 249
 
2.5%
5 558
 
5.6%
4 981
 
9.8%
3 1615
16.2%
2 2232
22.3%
1 2694
26.9%

Interactions

2023-06-26T15:01:19.517399image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:00:52.662387image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:00:54.398206image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:00:56.137987image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:00:58.229704image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:00.761379image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:02.726454image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:04.588021image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:06.629459image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:08.356837image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:10.189990image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:11.984869image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:13.925511image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:15.685935image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:17.546938image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:19.627798image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2023-06-26T15:01:02.612373image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:04.456737image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:06.512475image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:08.244937image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:10.080273image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:11.869616image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:13.800414image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:15.565654image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:17.438136image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:19.404045image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-06-26T15:01:30.116879image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
EstabTermlySessionsPossible/\BLCEIMGODXYGenderEnrolStatusNCyearActualPVWHSTU
Estab1.0000.096-0.003-0.026-0.044-0.075-0.018-0.029-0.027-0.0090.010-0.006-0.106-0.0050.2030.0000.0140.0510.0080.0430.0060.0150.0160.0000.016
TermlySessionsPossible0.0961.0000.5340.565-0.065-0.0100.0580.0000.0060.004-0.025-0.064-0.144-0.428-0.0770.0290.0000.0160.0110.0350.0250.0000.0210.0000.037
/-0.0030.5341.0000.900-0.151-0.206-0.144-0.087-0.173-0.061-0.072-0.267-0.188-0.230-0.0420.0090.0110.0390.0000.0240.0210.0000.0200.0230.062
\-0.0260.5650.9001.000-0.159-0.074-0.158-0.065-0.161-0.054-0.063-0.255-0.194-0.244-0.0360.0230.0240.0390.0100.0120.0230.0000.0000.0210.039
B-0.044-0.065-0.151-0.1591.0000.0150.0470.015-0.0050.0020.0110.0180.1520.012-0.0070.0000.0330.0000.0320.0170.0220.0000.0000.0150.000
L-0.075-0.010-0.206-0.0740.0151.0000.0410.1020.1090.0330.0300.139-0.0000.110-0.0160.0000.0100.0220.0250.0060.0000.0230.0470.0000.065
C-0.0180.058-0.144-0.1580.0470.0411.0000.0430.0620.0350.0160.0880.093-0.009-0.0110.0070.0110.0160.0000.0000.0170.0000.0120.0110.000
E-0.0290.000-0.087-0.0650.0150.1020.0431.0000.032-0.0070.0170.1130.0580.0030.0010.0040.0070.0110.0000.0260.0110.0000.1280.0000.020
I-0.0270.006-0.173-0.161-0.0050.1090.0620.0321.0000.093-0.0040.133-0.0000.0840.0260.0140.0050.0130.0000.0140.0130.0160.0250.0000.020
M-0.0090.004-0.061-0.0540.0020.0330.035-0.0070.0931.0000.0180.028-0.0220.013-0.0140.0200.0230.0180.0000.0080.0000.0000.0000.0110.019
G0.010-0.025-0.072-0.0630.0110.0300.0160.017-0.0040.0181.0000.0440.0280.0230.0220.0000.0000.0030.0080.0000.0000.0000.0000.0100.000
O-0.006-0.064-0.267-0.2550.0180.1390.0880.1130.1330.0280.0441.0000.0460.0770.0550.0270.0110.0140.0130.0020.0070.0200.0000.0000.066
D-0.106-0.144-0.188-0.1940.152-0.0000.0930.058-0.000-0.0220.0280.0461.000-0.039-0.0130.0190.0260.0000.0000.0000.0000.0000.0000.0100.000
X-0.005-0.428-0.230-0.2440.0120.110-0.0090.0030.0840.0130.0230.077-0.0391.0000.0240.0000.0000.0060.0000.0210.0170.0140.0090.0210.022
Y0.203-0.077-0.042-0.036-0.007-0.016-0.0110.0010.026-0.0140.0220.055-0.0130.0241.0000.0000.0130.0200.0130.0000.0200.0000.0130.0300.000
Gender0.0000.0290.0090.0230.0000.0000.0070.0040.0140.0200.0000.0270.0190.0000.0001.0000.0000.0170.0000.0000.0000.0280.0000.0000.000
EnrolStatus0.0140.0000.0110.0240.0330.0100.0110.0070.0050.0230.0000.0110.0260.0000.0130.0001.0000.0090.0000.0090.0100.0020.0000.0000.007
NCyearActual0.0510.0160.0390.0390.0000.0220.0160.0110.0130.0180.0030.0140.0000.0060.0200.0170.0091.0000.0130.0060.0000.0210.0000.0160.017
P0.0080.0110.0000.0100.0320.0250.0000.0000.0000.0000.0080.0130.0000.0000.0130.0000.0000.0131.0000.0000.0000.0000.0000.0150.000
V0.0430.0350.0240.0120.0170.0060.0000.0260.0140.0080.0000.0020.0000.0210.0000.0000.0090.0060.0001.0000.0180.0000.0060.0000.008
W0.0060.0250.0210.0230.0220.0000.0170.0110.0130.0000.0000.0070.0000.0170.0200.0000.0100.0000.0000.0181.0000.0200.0000.0080.000
H0.0150.0000.0000.0000.0000.0230.0000.0000.0160.0000.0000.0200.0000.0140.0000.0280.0020.0210.0000.0000.0201.0000.0000.0000.011
S0.0160.0210.0200.0000.0000.0470.0120.1280.0250.0000.0000.0000.0000.0090.0130.0000.0000.0000.0000.0060.0000.0001.0000.0000.008
T0.0000.0000.0230.0210.0150.0000.0110.0000.0000.0110.0100.0000.0100.0210.0300.0000.0000.0160.0150.0000.0080.0000.0001.0000.016
U0.0160.0370.0620.0390.0000.0650.0000.0200.0200.0190.0000.0660.0000.0220.0000.0000.0070.0170.0000.0080.0000.0110.0080.0161.000

Missing values

2023-06-26T15:01:21.462339image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-06-26T15:01:21.965176image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

EstabUPNSurnameForenameMiddlenamesPreferredSurnameFormerSurnameGenderDoBEnrolStatusEntryDateNCyearActualTermlySessionsPossible/\BJLPVWCEHIMRSTGNOUDXY
02230502f339019-1f3f-4713-bb75-a8ca1bc7e238NaNNaNNaNNaNNaNFNaNC2016-09-01 00:00:0081176970000030211000020061142
11488093c24552c-9677-434e-aabc-917a7e1d4565NaNNaNNaNNaNNaNFNaNC2016-09-05 00:00:001414471710061114109100120140123
2188143074c75ab-7f8c-4272-a5d7-fd1f4fde88ffNaNNaNNaNNaNNaNMNaNLeaver2016-09-02 00:00:00Leaver12643444021211101200030715180
31529327751bb61-7104-4271-820f-679ea0c8eeedNaNNaNNaNNaNNaNMNaNC2018-09-04 00:00:00810747416040023101010013050362
422736326ffe55c-8c00-46a0-bb1a-84143011f672NaNNaNNaNNaNNaNMNaNLeaver2016-09-01 00:00:001014468673000203005200120170025
5199188610354d9-681b-46f7-9daf-88eccb954dd1NaNNaNNaNNaNNaNFNaNC2017-09-06 00:00:007123403760400091051001202413130
62141626ced5494-ad12-480b-925b-a2dbe195f2e7NaNNaNNaNNaNNaNFNaNLeaver2016-09-05 00:00:001195524730500000070011101108175
7240265a12b2101-8475-40a9-8482-f384ce0185ebNaNNaNNaNNaNNaNMNaNC2018-09-07 00:00:00913871711050116102200000211242
8173192b3851523-a578-4af2-8dba-31be39740514NaNNaNNaNNaNNaNMNaNC2020-09-01 00:00:001112144510015110910130011204012133
9157525b96e7ab2-1ebf-4bbe-b4bb-3283c5b3a5b9NaNNaNNaNNaNNaNFNaNLeaver2019-09-03 00:00:00Leaver109403850401011109100100703213
EstabUPNSurnameForenameMiddlenamesPreferredSurnameFormerSurnameGenderDoBEnrolStatusEntryDateNCyearActualTermlySessionsPossible/\BJLPVWCEHIMRSTGNOUDXY
99901618883c8edf8d-089f-4d5a-9503-58a6dd1aa1c5NaNNaNNaNNaNNaNFNaNC2019-09-04 00:00:00712659652010002210100000000200
99911335096d3d5b57-f163-44fb-845a-fbb1b60cd9cfNaNNaNNaNNaNNaNFNaNC2020-09-03 00:00:009124545720712140021010101212252
9992127415f21303cb-4010-4074-b2fc-ad5f4d7186e0NaNNaNNaNNaNNaNFNaNLeaver2019-09-04 00:00:00711851528080001111500001011143
99931460952d221e77-22de-4ffa-ae91-cdaed0bfddadNaNNaNNaNNaNNaNMNaNC2017-09-06 00:00:007116566010600103011001020102162
999416563365bded21-b58b-48eb-9724-7b51ed53d73aNaNNaNNaNNaNNaNMNaNC2018-09-07 00:00:00713263601021102018100020610160
9995237844d6c47abb-dfc7-4a29-a98c-3f67543970e8NaNNaNNaNNaNNaNMNaNC2016-09-01 00:00:009136646590110110002011101110143
9996169480d40c55c3-6359-496a-a3f7-586a81b43ed9NaNNaNNaNNaNNaNMNaNLeaver2017-09-06 00:00:00101336467005121010100001070621
999722408207f2875f-e452-42d4-9216-d82ec564e977NaNNaNNaNNaNNaNMNaNC2018-09-04 00:00:00111095351707011400910000012113221
9998209094f4535c43-40de-4c83-b7ca-4d1a181b607bNaNNaNNaNNaNNaNFNaNC2016-09-05 00:00:001014271710030102005200120514151
999915603812ebb1e0-328e-4d8b-8c1e-2e9990c2613cNaNNaNNaNNaNNaNMNaNC2020-09-02 00:00:0011114445320611260013101100713122